code: def run(raw_data):
      # Get predictions and explanations for each data point
      data = pd.read_json(raw_data)
      # Make prediction
      predictions = original_model.predict(data)
      # Retrieve model explanations
      local_importance_values = scoring_explainer.explain(data)
      # You can return any data type as long as it is JSON-serializable
      return {
          "predictions": predictions.tolist(),
          "local_importance_values": local_importance_values,
      }
  
file_path: MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/scoring-time/score_remote_explain.py
imports: import json
  import numpy
  import pandas
  import os
  import pickle
  import joblib
  from sklearn.linear_model import LogisticRegression
  from azureml.core.model import Model
label_desc: Use image node_id 2 for calling a global function with example usage: run(raw_data) and returns: dict